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INTRUSION SENSORS strive to have a high detection rate and low false alarm rate.

By Eric Olson and Steven Pisciotta

Ongoing threats from terrorist activities at critical facilities require early detection before the threats can reach their target and complete their mission. This has produced the need for advanced security systems to effectively detect terrorist activity, while reducing alarms caused by normal friendly activity. Automatic Threat Assessment, also referred to as Identify Friend or Foe (IFF), is the ability to automatically acknowledge alarms created by friendly assets. It can be achieved with a security system that uses GPS and geospatial data to go beyond the typical intrusion-sensor-only configuration.

The addition of a tracking system associated with friendly vehicles and personnel can provide the missing information necessary to tighten security and reduce the need to take action on alarms caused by friendly targets, and reduce the material and personnel cost of threat assessment. Tracking systems and intrusion sensors can worktogether to automatically classify an actual intruder with high confidence and without operator intervention.

The Verification Problem

Typical intrusion sensors include intelligent fences, ground proximity sensors, radar, LIDAR, and video analytics. The role of the intrusion sensor is to identify a breach and notify security personnel so they may perform verification. Table 1 shows the formal alarm types received from intrusion sensors, which strive for a high detection rate and a low false-alarm rate. For this reason, the nuisance alarm can be problematic as it reflects a real event for the intrusion sensor, but often a non-event for the security operator.

These typical sensors only provide a “suspected intruder” list. The follow-on task is to decide whether or not to reclassify a suspected intruder as an actual intruder. This process is typically a manual task and can be difficult, confusing, and time-consuming.

For instance, a landscape crew will trigger alarms. Even for very accurate systems that can uniquely track the object over a long period, it is highly likely that over the period of time the landscapers are in the area, the track will be lost, causing the system to re-alarm on the same person or vehicle, as it represents a potential intrusion.

If the landscaping crew needs to open a gate, and that gate is integrated into the facility’s access control system via a dry contact or beam breaker device, it may continuously alarm while left open, or at a minimum, in the case of the beam, each time one of the workers or the vehicle passes through the entrance. In these situations, security will either need to validate each alarm by verifying it on a camera or having an officer follow the landscaping crew throughout their route.

The existence of a friendly alarm event that needs continual validation can lead to compacency of security personnel, either not verifying it, or not verifying it in a timely manner.

Table 1. Alarm types.

Combined Detection, Location

A GPS tracking system combined with the intrusion sensors can help identify friends. Tracking systems consist of two main types of locating devices: GPS-enabled devices and wireless transponders.

Modern, low-cost GPS receivers can achieve an accuracy rating of less than 3 meters, provide an update once per second, and do not require visibility to the open sky. Wireless communication transmits the GPS data to the C2 system. A typical data set includes time, date, latitude, longitude, altitude, heading, speed, and quality of GPS signal.

The combination of intrusion sensors and tracking systems can produce automatic threat assessment. Routine situations requiring significant security involvement, such as the landscaping scenario, can be automatically managed by the system. The command and control system has the ability to know friendly targets and their location.

Further, the system can perform a check before actually alarming. In the case of a perimeter alarm, it now has the intelligence to understand, within a level of confidence, that the object detected by the intrusion sensors is the same friendly item being tracking by the tracking system. If the system determines the targets to be the same object, the alarm can be suppressed, eliminating the need for security to verify the event.

THE COMBINATION of intrusion sensors and a tracking system allows for Automatic Threat Detection.

Common Operating Picture

The integration of these types of systems is not complex in terms of how to coordinate data. Interface documents exist for these types of integration and are done on a regular basis. Typical position and target information is communicated over XML in a standard format. However, to gain these benefits, the tracking systems and intrusion sensors must all work within a common geospatial operating picture.

Advantages of geospatial or geo-referenced systems systems include the ability to easily display and control data in a map-based format, allowing tracking systems and intrusion sensors to synergistically perform automatic verification. This combined knowledge of the target’s track also allows the fusing of the GPS data and the intrusion sensor data into a single object and path, aiding security by reducing target and track clutter on his command and control or PSIM (perimeter security information system).

Take for example a guard enabled with a tracking device, performing a tour around a fence protected by video analytics enabled cameras. On a typical PSIM, a normal guard tour would result in two icons on the display, one friendly from the tracking system and one unknown from the video analytics. This scenario would also result in two similar object tracks. Security would need to review the situation and understand that this symbology represents a single target and a single track.

Integrating the tracking system with the video analytics system allows for a fusing of this data, and the resulting command-and-control symbology is a single target and a single track.

Other considerations when combining a tracking system with intrusion sensors include update rate, time and location accuracies, and overlapping coverage.

Ideally, all sensors would be synchronized when it comes to timing aspects, but this is typically not the case. Different timing between data updates and time inaccuracies can result in the inability for the systems to confidently conclude that two tracks were created by the same target. Transport delay, the transmission of the GPS data through the satellite, can also be an issue. For tracking devices, it’s vital for the data to be received by the C2 system with a repeatable transport delay. Variability in the transport delay also decreases the ability to automatically verify the threat.

Geographic accuracy of both the GPS tracker and the intrusion sensor is another important factor in data fusion. Typical GPS trackers have an accuracy rating of 3–10 meters. Actual accuracy varies based upon the visible GPS satellites, tall buildings, body worn, and RF interference. Intrusion sensors also possess an inherent accuracy. Radar surveillance may have a resolution of 1 x 1 meter at close range, but it expands at far range to 1 x 20 meters.

Intelligent fence sensors and video analytic systems can have resolutions that vary from 1 to 25 meters, based on the type of sensor and the terrain. These geographic inaccuracies can be handled to some degree by considering other factors, including heading, speed, and previous track, but it’s important to understand where these inaccuracies can occur.

Overlapping coverage of surveillance sensors also affects data fusion. In the case of track fusion, this ability is only available is areas where both a geospatial intrusion sensor exists and a tracking system is operational. If there are gaps in overlapping coverage, or areas that do not include geospatial- based intrusion sensors, then fusion is not possible in those regions.